Channel Max Pooling for Image Classification

Cheng, Leping; Chang, Dongliang; Xie, Jiyang; Ma, R.; Wu, Chunsheng; Ma, Zhanyu

doi:10.1007/978-3-030-36189-1_23

Cited by 4 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, global pooling methods are more robust to spatial translations of the input and prevent overfitting. Channel pooling methods include channel average pooling (CAP) [44] and channel max pooling (CMP) [45], which perform feature fusion by computing average or maximum pixel values, respectively, at the same positions in each channel of feature maps. Furthermore, these methods only compress features and do not contain learnable weights, leading to poor classification results.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Lin

Jhang

2022

IEEE Access

View full text Add to dashboard Cite

With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. Consequently, many traffic-related problems have emerged, such as traffic jams and excessive numbers and types of vehicles. To solve traffic problems, road data collection is important. Therefore, in this paper, we develop an intelligent traffic-monitoring system based on you only look once (YOLO) and a convolutional fuzzy neural network (CFNN), which record traffic volume, and vehicle type information from the road. In this system, YOLO is first used to detect vehicles and is combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector-CFNN) and a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed method achieved an accuracy of 90.45% on the Beijing Institute of Technology public dataset. On the GRAM-RTM data set, the mean average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods are 99%, superior to those of other methods. On actual roads in Taiwan, the proposed YOLO-CFNN and YOLO-VCFNN methods not only have a high F1 score for vehicle classification but also have outstanding accuracy in vehicle counting. In addition, the proposed system can maintain a detection speed of more than 30 frames per second in the AGX embedded platform. Therefore, the proposed intelligent traffic monitoring system is suitable for real-time vehicle classification and counting in the actual environment.

show abstract

Section: Literature Reviewmentioning

confidence: 99%